Deep learning for lung Cancer detection and classification
Lung cancer is one of the main reasons for death in the world among both men and women, with an impressive rate of about five million deadly cases per year. Computed Tomography (CT) scan can provide valuable information in the diagnosis of lung diseases. The main objective of this work is to detect...
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| Published in | Multimedia tools and applications Vol. 79; no. 11-12; pp. 7731 - 7762 |
|---|---|
| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
New York
Springer US
01.03.2020
Springer Nature B.V |
| Subjects | |
| Online Access | Get full text |
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| Abstract | Lung cancer is one of the main reasons for death in the world among both men and women, with an impressive rate of about five million deadly cases per year. Computed Tomography (CT) scan can provide valuable information in the diagnosis of lung diseases. The main objective of this work is to detect the cancerous lung nodules from the given input lung image and to classify the lung cancer and its severity. To detect the location of the cancerous lung nodules, this work uses novel Deep learning methods. This work uses best feature extraction techniques such as Histogram of oriented Gradients (HoG), wavelet transform-based features, Local Binary Pattern (LBP), Scale Invariant Feature Transform (SIFT) and Zernike Moment. After extracting texture, geometric, volumetric and intensity features, Fuzzy Particle Swarm Optimization (FPSO) algorithm is applied for selecting the best feature. Finally, these features are classified using Deep learning. A novel FPSOCNN reduces computational complexity of CNN. An additional valuation is performed on another dataset coming from Arthi Scan Hospital which is a real-time data set. From the experimental results, it is shown that novel FPSOCNN performs better than other techniques. |
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| AbstractList | Lung cancer is one of the main reasons for death in the world among both men and women, with an impressive rate of about five million deadly cases per year. Computed Tomography (CT) scan can provide valuable information in the diagnosis of lung diseases. The main objective of this work is to detect the cancerous lung nodules from the given input lung image and to classify the lung cancer and its severity. To detect the location of the cancerous lung nodules, this work uses novel Deep learning methods. This work uses best feature extraction techniques such as Histogram of oriented Gradients (HoG), wavelet transform-based features, Local Binary Pattern (LBP), Scale Invariant Feature Transform (SIFT) and Zernike Moment. After extracting texture, geometric, volumetric and intensity features, Fuzzy Particle Swarm Optimization (FPSO) algorithm is applied for selecting the best feature. Finally, these features are classified using Deep learning. A novel FPSOCNN reduces computational complexity of CNN. An additional valuation is performed on another dataset coming from Arthi Scan Hospital which is a real-time data set. From the experimental results, it is shown that novel FPSOCNN performs better than other techniques. |
| Author | Srinivasan, Andy Asuntha, A. |
| Author_xml | – sequence: 1 givenname: A. surname: Asuntha fullname: Asuntha, A. email: asuntha.srm@gmail.com organization: Department of Electronics & Instrumentation Engineering, SRM Institute of Science & Technology – sequence: 2 givenname: Andy surname: Srinivasan fullname: Srinivasan, Andy organization: Department of Electronics & Instrumentation Engineering, Valliammai Engineering College |
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| Cites_doi | 10.1007/s11042-018-6673-2 10.1148/radiol.2283020505 10.1109/ISBI.2015.7163869 10.1109/WiSPNET.2016.7566533 10.1007/s10278-009-9185-9 10.1016/j.jvcir.2017.09.008 10.1088/0031-9155/56/4/016 10.1049/iet-ipr.2016.1014 10.1590/2446-4740.04615 10.1109/TKDE.2015.2399298 10.1007/s11277-017-4981-x 10.1016/j.patcog.2014.12.016 10.1016/j.acra.2009.12.009 10.1080/0952813X.2015.1020526 10.1007/s11265-016-1134-5 10.1109/CCAA.2015.7148560 10.1186/s12938-015-0003-y 10.1109/CRV.2015.25 10.1145/2733373.2806217 10.1007/s11042-017-4480-9 10.1016/j.procs.2017.12.016 10.1016/S0933-3657(01)00094-X 10.1016/j.mspro.2015.06.077 10.1109/ICACDOT.2016.7877572 10.1109/TIP.2008.919949 10.1109/ICACCI.2015.7275773 10.1007/s11517-018-1841-0 10.1155/2017/8314740 10.1109/ICCSNT.2012.6526151 10.1109/ISSPA.2012.6310676 10.1109/GCCT.2015.7342723 10.1109/TMI.2005.852048 |
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| References | Li X-X, Li B, Tian L-F, Zhang L (2018) Automatic benign and malignant classification of pulmonary nodules in thoracic computed tomography based on RF algorithm. IET Image Process. https://doi.org/10.1049/iet-ipr.2016.1014 Song QZ, Zhao L, Luo XK, Dou XC (2017) Using deep learning for classification of lung nodules on computed tomography images. Journal of healthcare engineering. https://doi.org/10.1155/2017/8314740 OrozcoHMVillegasOOVSánchezVGCde Jesús Ochoa DomínguezHde Jesús Nandayapa AlfaroMAutomated system for lung nodules classification based on wavelet feature descriptor and support vector machineBiomed Eng201514912010.1186/s12938-015-0003-y Shao H, Cao L, Liu Y (2012) A detection approach for solitary pulmonary nodules based on CT images. IEEE, 2nd international conference on computer science and network technology. da SilvaGLFde Carvalho FilhoAOSilvaACde PaivaACGattassMTaxonomic indexes for differentiating malignancy of lung nodules on CT imagesResearch on Biomedical Engineering201632326327210.1590/2446-4740.04615 Zhang B, Allebach JP (2008) Adaptive Bilateral Filter for Sharpness Enhancement and Noise Removal. IEEE Trans Image Process 17(5) Ani Brown MaryNDharmaD‘Coral reef image classification employing improved LDP for feature extraction’, ElsevierJ Vis Commun Image Represent201749C22524210.1016/j.jvcir.2017.09.008 de Sousa CostaRWda SilvaGLFde Carvalho FilhoAOSilvaACde Paiva Marcelo GattassAC“Classification of malignant and benign lung nodules using taxonomic diversity index and phylogenetic distance”, springerMed Biol Eng Comput201856112125213610.1007/s11517-018-1841-0 Kumar D, Wong A, Clausi DA (2015) “Lung nodule classification using deep features in CT images”, IEEE, 12th conference on computer robot vision, pp 133-138. DOI. https://doi.org/10.1109/CRV.2015.25 ParkSCTanJWangXLedermanDLeaderJKKimSHZhengBComputer-aided detection of early interstitial lung diseases using low-dose CT imagesPhys Med Biol2011561139115310.1088/0031-9155/56/4/016Iop Publishing Roy TS, Sirohi N, Patle A (2015) Classification of Lung Image and Nodule Detection Using Fuzzy Inference System. IEEE, International Conference on Computing, Communication and Automation (ICCCA2015), pp. 1204–1207 Van Ginneken B, Setio AAA, Jacobs C, Ciompi F (2015) Off-the-shelf convolutional neural network features for pulmonary nodule detection in computed tomography scans. IEEE 12th International Symposium on Biomedical Imaging (ISBI). doi:10.1109/isbi.2015.7163869 ZhuYTanYHuaYWangMZhangGZhangJFeature selection and performance evaluation of support vector machine (SVM)-based classifier for differentiating benign and malignant pulmonary nodules by computed tomographyJ Digit Imaging2010231516510.1007/s10278-009-9185-9 ZhouZ-HJiangYYangY-BChenS-FLung cancer cell identification based on artificial neural network ensemblesArtif Intell Med200224253610.1016/S0933-3657(01)00094-XElsevier MakajuSPrasadAAElchouemiSLung Cancer detection using CT scan imagesElsevier, Procedia Computer Science201812510711410.1016/j.procs.2017.12.016 Dong-ping Tian and Nai-qian Li, 2009, ‘Fuzzy Particle Swarm Optimization Algorithm’, IEEE, International Joint Conference on Artificial Intelligence, pp. 263–267. Jin X-Y, Zhang Y-C, Jin Q-L (2016) Pulmonary nodule detection based on CT images using Convolution neural network. IEEE, 9th International Symposium on Computational Intelligence and Design, pp. 202–204. de Carvalho FilhoAOSilvaACde PaivaACNunesRAGattassMLung-nodule classification based on computed tomography using taxonomic diversity indexes and an SVMSpringer, Journal of Signal Processing Systems, DOI20168717919610.1007/s11265-016-1134-5 Sangamithraa, Govindaraju (2016) Lung tumour detection and classification using EK-mean clustering. IEEE WiSPNET BhuvaneswariBTDetection of Cancer in lung with K-NN classification using genetic algorithmProcedia Mater Sci201510433440370179810.1016/j.mspro.2015.06.077Elsevier BrownAMaryNDejeyD‘Classification of coral reef submarine images and videos using a novel Z with tilted Z local binary pattern (Z⊕TZLBP)’, springerWirel Pers Commun20189832427245910.1007/s11277-017-4981-x ChabatFYangG-ZHansellDMObstructive lung diseases: texture classification for differentiation at CT1Radiology2003228387187710.1148/radiol.2283020505 Nie L, Zhang L, Yang Y, Wang M, Hong R, Chua T-S (2015) Beyond doctors: future health prediction from multimedia and multimodal observations. proceedings of the 23rd ACM international conference on multimedia. Ignatious S, Joseph R (2015) Computer Aided Lung Cancer Detection System. IEEE, Proceedings of 2015 Global Conference on Communication Technologies (GCCT 2015), pp. 555–558. NieLWangMZhangLYanSZhangBChuaT-SDisease inference from health-related questions via sparse deep learningIEEE Trans Knowl Data Eng20152782107211910.1109/TKDE.2015.2399298 Zhang L, Zhang Q, Zhang L, Tao D, Huang X, Bo D (2014) Ensemble manifold regularized sparse low-rank approximation for multiview feature embedding”, Elsevier. Pattern Recogn. https://doi.org/10.1016/j.patcog.2014.12.016 Orozco HM, Villegas OOV, Maynez LO, Sanchez VGC, de Jesus Ochoa Dominguez H (2012) Lung Nodule CLASSIFICATION in Frequency Domain Using Support Vector Machine. IEEE, In international conference on information science, signal processing and their application. Zhang L, Zhang Q, Du Member B, Huang X, Tang YY, Tao D (2016) Simultaneous spectral-spatial feature selection and extraction for Hyperspectral images. IEEE Transactions on Cybernetics AkramSJavedMYHussainARiazFUsman AkramMIntensity-based statistical features for classification of lungs CT scan nodules using artificial intelligence techniquesJournal of Experimental & Theoretical Artificial Intelligence201527673775110.1080/0952813X.2015.1020526 Ani Brown MaryNDejeyD‘Classification of coral reef submarine images and videos using a novel Z with tilted Z local binary pattern (Z⊕TZLBP)’, springerWirel Pers Commun20189832427245910.1007/s11277-017-4981-x Aggarwal T, Furqan A, Kalra K (2015) Feature extraction and LDA based classification of lung nodules in chest CT scan images. IEEE, International Conference on Advances in Computing, Communications and Informatics (ICACCI), pp. 1189–1193 Sun W, Zheng B, Qian W (2016) "computer aided lung cancer diagnosis with deep learning algorithms" International Society for Optics and Photonics, medical imaging : computer-aided diagnosis. Vol. 9785 AlakwaaWNassefMBadrALung Cancer detection and classification with 3D convolutional neural network (3D-CNN)International Journal of Advanced Computer Science and Applications (IJACSA)201788409417 Ani Brown Mary N, Dharma D (2018) A novel framework for real-time diseased coral reef image classification’, Springer. Multimed Tools Appl:1–39. https://doi.org/10.1007/s11042-018-6673-2 SuzukiKLiFSoneSDoiKComputer-aided diagnostic scheme for distinction between benign and malignant nodules in thoracic low-dose CT by use of massive training artificial neural networkIEEE Trans Med Imaging20052491138115010.1109/TMI.2005.852048 Dhaware BU, Pise AC, (2016) Lung Cancer Detection Using Bayasein Classifier and FCM Segmentation. IEEE, International Conference on Automatic Control and Dynamic Optimization Techniques (ICACDOT), pp. 170–174 Da SilvaGLFda Silva NetoOPSilvaACde Paiva Marcelo GattassAC“Lung nodules diagnosis based on evolutionary convolutional neural network”, springerMultimed Tools Appl20177618190391905510.1007/s11042-017-4480-9 Chen H, Xu Y, Ma Y, Ma B (2010) Neural Network Ensemble-Based Computer-Aided Diagnosis for Differentiation of Lung Nodules on CT Images. Acad Radiol 17(5) W Alakwaa (8394_CR3) 2017; 8 GLF da Silva (8394_CR12) 2016; 32 L Nie (8394_CR21) 2015; 27 8394_CR19 N Ani Brown Mary (8394_CR4) 2018; 98 8394_CR27 8394_CR26 8394_CR6 8394_CR29 8394_CR28 8394_CR23 SC Park (8394_CR25) 2011; 56 8394_CR1 8394_CR22 Z-H Zhou (8394_CR37) 2002; 24 HM Orozco (8394_CR24) 2015; 14 S Makaju (8394_CR20) 2018; 125 A Brown (8394_CR8) 2018; 98 F Chabat (8394_CR9) 2003; 228 S Akram (8394_CR2) 2015; 27 Y Zhu (8394_CR38) 2010; 23 N Ani Brown Mary (8394_CR5) 2017; 49 RW de Sousa Costa (8394_CR14) 2018; 56 8394_CR16 8394_CR15 8394_CR18 8394_CR17 8394_CR34 8394_CR33 8394_CR36 8394_CR35 8394_CR30 BT Bhuvaneswari (8394_CR7) 2015; 10 8394_CR10 GLF Da Silva (8394_CR11) 2017; 76 K Suzuki (8394_CR31) 2005; 24 8394_CR32 AO de Carvalho Filho (8394_CR13) 2016; 87 |
| References_xml | – reference: BrownAMaryNDejeyD‘Classification of coral reef submarine images and videos using a novel Z with tilted Z local binary pattern (Z⊕TZLBP)’, springerWirel Pers Commun20189832427245910.1007/s11277-017-4981-x – reference: Chen H, Xu Y, Ma Y, Ma B (2010) Neural Network Ensemble-Based Computer-Aided Diagnosis for Differentiation of Lung Nodules on CT Images. Acad Radiol 17(5) – reference: Dhaware BU, Pise AC, (2016) Lung Cancer Detection Using Bayasein Classifier and FCM Segmentation. IEEE, International Conference on Automatic Control and Dynamic Optimization Techniques (ICACDOT), pp. 170–174 – reference: Van Ginneken B, Setio AAA, Jacobs C, Ciompi F (2015) Off-the-shelf convolutional neural network features for pulmonary nodule detection in computed tomography scans. IEEE 12th International Symposium on Biomedical Imaging (ISBI). doi:10.1109/isbi.2015.7163869 – reference: ZhuYTanYHuaYWangMZhangGZhangJFeature selection and performance evaluation of support vector machine (SVM)-based classifier for differentiating benign and malignant pulmonary nodules by computed tomographyJ Digit Imaging2010231516510.1007/s10278-009-9185-9 – reference: SuzukiKLiFSoneSDoiKComputer-aided diagnostic scheme for distinction between benign and malignant nodules in thoracic low-dose CT by use of massive training artificial neural networkIEEE Trans Med Imaging20052491138115010.1109/TMI.2005.852048 – reference: BhuvaneswariBTDetection of Cancer in lung with K-NN classification using genetic algorithmProcedia Mater Sci201510433440370179810.1016/j.mspro.2015.06.077Elsevier – reference: OrozcoHMVillegasOOVSánchezVGCde Jesús Ochoa DomínguezHde Jesús Nandayapa AlfaroMAutomated system for lung nodules classification based on wavelet feature descriptor and support vector machineBiomed Eng201514912010.1186/s12938-015-0003-y – reference: ParkSCTanJWangXLedermanDLeaderJKKimSHZhengBComputer-aided detection of early interstitial lung diseases using low-dose CT imagesPhys Med Biol2011561139115310.1088/0031-9155/56/4/016Iop Publishing – reference: Sun W, Zheng B, Qian W (2016) "computer aided lung cancer diagnosis with deep learning algorithms" International Society for Optics and Photonics, medical imaging : computer-aided diagnosis. Vol. 9785 – reference: Ani Brown Mary N, Dharma D (2018) A novel framework for real-time diseased coral reef image classification’, Springer. Multimed Tools Appl:1–39. https://doi.org/10.1007/s11042-018-6673-2 – reference: Ani Brown MaryNDharmaD‘Coral reef image classification employing improved LDP for feature extraction’, ElsevierJ Vis Commun Image Represent201749C22524210.1016/j.jvcir.2017.09.008 – reference: Da SilvaGLFda Silva NetoOPSilvaACde Paiva Marcelo GattassAC“Lung nodules diagnosis based on evolutionary convolutional neural network”, springerMultimed Tools Appl20177618190391905510.1007/s11042-017-4480-9 – reference: Jin X-Y, Zhang Y-C, Jin Q-L (2016) Pulmonary nodule detection based on CT images using Convolution neural network. IEEE, 9th International Symposium on Computational Intelligence and Design, pp. 202–204. – reference: Sangamithraa, Govindaraju (2016) Lung tumour detection and classification using EK-mean clustering. IEEE WiSPNET – reference: Aggarwal T, Furqan A, Kalra K (2015) Feature extraction and LDA based classification of lung nodules in chest CT scan images. IEEE, International Conference on Advances in Computing, Communications and Informatics (ICACCI), pp. 1189–1193 – reference: de Carvalho FilhoAOSilvaACde PaivaACNunesRAGattassMLung-nodule classification based on computed tomography using taxonomic diversity indexes and an SVMSpringer, Journal of Signal Processing Systems, DOI20168717919610.1007/s11265-016-1134-5 – reference: AlakwaaWNassefMBadrALung Cancer detection and classification with 3D convolutional neural network (3D-CNN)International Journal of Advanced Computer Science and Applications (IJACSA)201788409417 – reference: ChabatFYangG-ZHansellDMObstructive lung diseases: texture classification for differentiation at CT1Radiology2003228387187710.1148/radiol.2283020505 – reference: Ani Brown MaryNDejeyD‘Classification of coral reef submarine images and videos using a novel Z with tilted Z local binary pattern (Z⊕TZLBP)’, springerWirel Pers Commun20189832427245910.1007/s11277-017-4981-x – reference: Roy TS, Sirohi N, Patle A (2015) Classification of Lung Image and Nodule Detection Using Fuzzy Inference System. IEEE, International Conference on Computing, Communication and Automation (ICCCA2015), pp. 1204–1207 – reference: Kumar D, Wong A, Clausi DA (2015) “Lung nodule classification using deep features in CT images”, IEEE, 12th conference on computer robot vision, pp 133-138. DOI. https://doi.org/10.1109/CRV.2015.25 – reference: da SilvaGLFde Carvalho FilhoAOSilvaACde PaivaACGattassMTaxonomic indexes for differentiating malignancy of lung nodules on CT imagesResearch on Biomedical Engineering201632326327210.1590/2446-4740.04615 – reference: Nie L, Zhang L, Yang Y, Wang M, Hong R, Chua T-S (2015) Beyond doctors: future health prediction from multimedia and multimodal observations. proceedings of the 23rd ACM international conference on multimedia. – reference: Li X-X, Li B, Tian L-F, Zhang L (2018) Automatic benign and malignant classification of pulmonary nodules in thoracic computed tomography based on RF algorithm. IET Image Process. https://doi.org/10.1049/iet-ipr.2016.1014 – reference: Orozco HM, Villegas OOV, Maynez LO, Sanchez VGC, de Jesus Ochoa Dominguez H (2012) Lung Nodule CLASSIFICATION in Frequency Domain Using Support Vector Machine. IEEE, In international conference on information science, signal processing and their application. – reference: Song QZ, Zhao L, Luo XK, Dou XC (2017) Using deep learning for classification of lung nodules on computed tomography images. Journal of healthcare engineering. https://doi.org/10.1155/2017/8314740 – reference: Zhang L, Zhang Q, Zhang L, Tao D, Huang X, Bo D (2014) Ensemble manifold regularized sparse low-rank approximation for multiview feature embedding”, Elsevier. Pattern Recogn. https://doi.org/10.1016/j.patcog.2014.12.016 – reference: Ignatious S, Joseph R (2015) Computer Aided Lung Cancer Detection System. IEEE, Proceedings of 2015 Global Conference on Communication Technologies (GCCT 2015), pp. 555–558. – reference: Shao H, Cao L, Liu Y (2012) A detection approach for solitary pulmonary nodules based on CT images. IEEE, 2nd international conference on computer science and network technology. – reference: ZhouZ-HJiangYYangY-BChenS-FLung cancer cell identification based on artificial neural network ensemblesArtif Intell Med200224253610.1016/S0933-3657(01)00094-XElsevier – reference: AkramSJavedMYHussainARiazFUsman AkramMIntensity-based statistical features for classification of lungs CT scan nodules using artificial intelligence techniquesJournal of Experimental & Theoretical Artificial Intelligence201527673775110.1080/0952813X.2015.1020526 – reference: NieLWangMZhangLYanSZhangBChuaT-SDisease inference from health-related questions via sparse deep learningIEEE Trans Knowl Data Eng20152782107211910.1109/TKDE.2015.2399298 – reference: Zhang B, Allebach JP (2008) Adaptive Bilateral Filter for Sharpness Enhancement and Noise Removal. IEEE Trans Image Process 17(5) – reference: de Sousa CostaRWda SilvaGLFde Carvalho FilhoAOSilvaACde Paiva Marcelo GattassAC“Classification of malignant and benign lung nodules using taxonomic diversity index and phylogenetic distance”, springerMed Biol Eng Comput201856112125213610.1007/s11517-018-1841-0 – reference: Dong-ping Tian and Nai-qian Li, 2009, ‘Fuzzy Particle Swarm Optimization Algorithm’, IEEE, International Joint Conference on Artificial Intelligence, pp. 263–267. – reference: Zhang L, Zhang Q, Du Member B, Huang X, Tang YY, Tao D (2016) Simultaneous spectral-spatial feature selection and extraction for Hyperspectral images. IEEE Transactions on Cybernetics – reference: MakajuSPrasadAAElchouemiSLung Cancer detection using CT scan imagesElsevier, Procedia Computer Science201812510711410.1016/j.procs.2017.12.016 – ident: 8394_CR6 doi: 10.1007/s11042-018-6673-2 – volume: 228 start-page: 871 issue: 3 year: 2003 ident: 8394_CR9 publication-title: Radiology doi: 10.1148/radiol.2283020505 – ident: 8394_CR33 doi: 10.1109/ISBI.2015.7163869 – ident: 8394_CR27 doi: 10.1109/WiSPNET.2016.7566533 – volume: 23 start-page: 51 issue: 1 year: 2010 ident: 8394_CR38 publication-title: J Digit Imaging doi: 10.1007/s10278-009-9185-9 – volume: 49 start-page: 225 issue: C year: 2017 ident: 8394_CR5 publication-title: J Vis Commun Image Represent doi: 10.1016/j.jvcir.2017.09.008 – ident: 8394_CR35 – volume: 56 start-page: 1139 year: 2011 ident: 8394_CR25 publication-title: Phys Med Biol doi: 10.1088/0031-9155/56/4/016 – volume: 8 start-page: 409 issue: 8 year: 2017 ident: 8394_CR3 publication-title: International Journal of Advanced Computer Science and Applications (IJACSA) – ident: 8394_CR19 doi: 10.1049/iet-ipr.2016.1014 – ident: 8394_CR30 – volume: 32 start-page: 263 issue: 3 year: 2016 ident: 8394_CR12 publication-title: Research on Biomedical Engineering doi: 10.1590/2446-4740.04615 – volume: 27 start-page: 2107 issue: 8 year: 2015 ident: 8394_CR21 publication-title: IEEE Trans Knowl Data Eng doi: 10.1109/TKDE.2015.2399298 – volume: 98 start-page: 2427 issue: 3 year: 2018 ident: 8394_CR8 publication-title: Wirel Pers Commun doi: 10.1007/s11277-017-4981-x – volume: 98 start-page: 2427 issue: 3 year: 2018 ident: 8394_CR4 publication-title: Wirel Pers Commun doi: 10.1007/s11277-017-4981-x – ident: 8394_CR36 doi: 10.1016/j.patcog.2014.12.016 – ident: 8394_CR10 doi: 10.1016/j.acra.2009.12.009 – volume: 27 start-page: 737 issue: 6 year: 2015 ident: 8394_CR2 publication-title: Journal of Experimental & Theoretical Artificial Intelligence doi: 10.1080/0952813X.2015.1020526 – volume: 87 start-page: 179 year: 2016 ident: 8394_CR13 publication-title: Springer, Journal of Signal Processing Systems, DOI doi: 10.1007/s11265-016-1134-5 – ident: 8394_CR26 doi: 10.1109/CCAA.2015.7148560 – ident: 8394_CR32 – volume: 14 start-page: 1 issue: 9 year: 2015 ident: 8394_CR24 publication-title: Biomed Eng doi: 10.1186/s12938-015-0003-y – ident: 8394_CR17 – ident: 8394_CR18 doi: 10.1109/CRV.2015.25 – ident: 8394_CR22 doi: 10.1145/2733373.2806217 – volume: 76 start-page: 19039 issue: 18 year: 2017 ident: 8394_CR11 publication-title: Multimed Tools Appl doi: 10.1007/s11042-017-4480-9 – volume: 125 start-page: 107 year: 2018 ident: 8394_CR20 publication-title: Elsevier, Procedia Computer Science doi: 10.1016/j.procs.2017.12.016 – volume: 24 start-page: 25 year: 2002 ident: 8394_CR37 publication-title: Artif Intell Med doi: 10.1016/S0933-3657(01)00094-X – volume: 10 start-page: 433 year: 2015 ident: 8394_CR7 publication-title: Procedia Mater Sci doi: 10.1016/j.mspro.2015.06.077 – ident: 8394_CR15 doi: 10.1109/ICACDOT.2016.7877572 – ident: 8394_CR34 doi: 10.1109/TIP.2008.919949 – ident: 8394_CR1 doi: 10.1109/ICACCI.2015.7275773 – volume: 56 start-page: 2125 issue: 11 year: 2018 ident: 8394_CR14 publication-title: Med Biol Eng Comput doi: 10.1007/s11517-018-1841-0 – ident: 8394_CR29 doi: 10.1155/2017/8314740 – ident: 8394_CR28 doi: 10.1109/ICCSNT.2012.6526151 – ident: 8394_CR23 doi: 10.1109/ISSPA.2012.6310676 – ident: 8394_CR16 doi: 10.1109/GCCT.2015.7342723 – volume: 24 start-page: 1138 issue: 9 year: 2005 ident: 8394_CR31 publication-title: IEEE Trans Med Imaging doi: 10.1109/TMI.2005.852048 |
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| SubjectTerms | Algorithms Computed tomography Computer Communication Networks Computer Science Data Structures and Information Theory Deep learning Feature extraction FPSO Histograms Image classification Image detection Lung cancer Lung diseases Machine learning Multimedia Information Systems Nodules Particle swarm optimization Special Purpose and Application-Based Systems Wavelet transforms |
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